CN115292965A - Least square regression-based dynamic photovoltaic model parameter identification method - Google Patents
Least square regression-based dynamic photovoltaic model parameter identification method Download PDFInfo
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Abstract
The invention provides a dynamic photovoltaic model parameter identification method based on least square regression, wherein a parameter identification process is based on a time domain simulation technology, the method utilizes the step response to load change in a linear region of the I-V characteristic of a photovoltaic source, and extracts the parameters of the dynamic part of a model by using the Least Square Regression (LSR) technology of experimental data. The method can effectively isolate the mutual influence of the inductor and the capacitor, and uses a load resistor to prevent the two poles of the circuit from influencing each other. The dynamic parameters can be identified by only measuring the load resistance once. The method for identifying the parameters of the dynamic photovoltaic model based on the least square regression can be suitable for a flexible and variable photovoltaic power generation system, can realize parameter identification by only one-time measurement, and greatly improves the identification efficiency.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a dynamic photovoltaic model parameter identification method based on least square regression.
Background
Two major survival crises faced by the society at present are environmental pollution and energy shortage, and the photovoltaic power generation technology is widely concerned as a pollution-free and renewable green power generation technology. The photovoltaic array is a core part of a photovoltaic system, and the output of a photovoltaic power supply is in a nonlinear relation with temperature and light intensity. Therefore, the photovoltaic output curve is explored and depicted, and the energy conversion rate of photovoltaic power generation can be greatly improved.
With the increasing scale and proportion of renewable energy sources connected to a power distribution network, new energy sources on the load side, mainly photovoltaic power generation, have more and more obvious influence on the power grid. The model parameters of the photovoltaic power generation system are the basis for steady-state, quasi-steady-state, and transient calculations, as well as modeling analysis, thereof. The accuracy of the model parameters has a very important influence on the power analysis planning research. Therefore, continuously improving the accuracy of the model parameters is significant to the practical application of the power system.
Research into photovoltaic resources, including issues related to maximizing power production and predicting the behavior of photovoltaic arrays under various environmental conditions, is receiving increasing attention. An accurate numerical model of the photovoltaic source is helpful for: analyzing the power converter of a photovoltaic plant, studying Maximum Power Point Tracking (MPPT) algorithms, etc. In the technical literature, several static photovoltaic models are available for selection. Of these models, the practical power application domain is used to reproduce a model of static I-V and P-V characteristics using a dynamic model of three parameters. These three dynamic parameters (capacitance, inductance and conductance) are not given in the manufacturer's data sheet. Therefore, a simple, fast and accurate parameter identification process is needed. However, methods for parameter identification by means of a single measurement have not been proposed in the technical literature.
Disclosure of Invention
In view of the above, the present invention provides a method for identifying parameters by a single measurement, which can simply, quickly and accurately implement a parameter identification process of a photovoltaic model.
In order to achieve the technical effects, the invention provides the following technical scheme:
the invention provides a least square regression-based dynamic photovoltaic model parameter identification method, which comprises the following steps of:
connecting a variable load resistor to a photovoltaic power supply to obtain a dynamic photovoltaic model, wherein the dynamic photovoltaic model utilizes a step response of a photovoltaic source I-V characteristic to load change to carry out parameter identification;
selecting a variable load resistance value based on the maximum power tracking-midpoint of the open circuit voltage arc;
performing reference test on the dynamic photovoltaic model to obtain a load current curve;
after the value of the load resistor is changed until an interactive pole appears, drawing a real curve of the current of the load at the moment and selecting an error value and a time interval;
performing a least squares regression calculation to the left of the load curve minimum based on the selected value of the load resistance;
and adjusting the time interval and calculating the actual error until the actual error is smaller than the selected error value to obtain a parameter identification result.
Furthermore, the dynamic photovoltaic model is obtained by simplifying a complete equivalent circuit of the photovoltaic module, and a voltage generator is used for replacing a current generator and a diode in the complete equivalent circuit of the photovoltaic module to obtain the equivalent circuit of the photovoltaic module in a linear voltage area, namely the dynamic photovoltaic model.
Further, the dynamic photovoltaic model specifically includes: a static model and a dynamic model;
the static model comprises four static parameters, specifically a series resistance of a photovoltaic unit, a series resistance of a parasitic capacitor, an open-circuit voltage and a load resistance;
the dynamic model includes two dynamic parameters, specifically an inductance and a capacitance.
Further, based on the selected value of the load resistance, performing least squares regression calculation on the left side of the minimum value of the load curve, specifically including:
establishing a calculation model of an inductance time constant, a capacitance time constant, an inductance coefficient and a capacitance coefficient in the dynamic model based on the dynamic photovoltaic model, and calculating four parameters by the calculation model based on the static parameters and the dynamic parameters;
and combining every two of the four parameters, and performing fitting calculation of least square regression respectively as horizontal and vertical coordinates of a least square method.
Further, the calculation model established based on the dynamic photovoltaic model specifically includes the following steps:
coefficient of capacitance
：
Coefficient of inductance
：
Time constant of capacitance
：
Time constant of inductance
：
In the above-mentioned respective formulas, the first and second,
in order to be said open-circuit voltage,
as the load resistance, there is a resistance of the load,
is the series resistance of the parasitic capacitance,
for the inductance in the dynamic model to be,
as is the capacitance in the dynamic model,
in order to be the load current,
wherein, in the step (A),
and connecting resistors in series for the photovoltaic units.
Further, two by two combinations are carried out from the four parameters, and the combinations are respectively used as horizontal and vertical coordinates of a least square method to carry out fitting calculation of least square regression, and the method specifically comprises the following steps:
drawing the dimensionality of the measured values of all the photovoltaic operation data;
calculating the average value of horizontal and vertical coordinates of all data points in the graph according to the following formula;
in the formula (I), the compound is shown in the specification,
and
respectively the average of the abscissa and ordinate,
and
are respectively the first
The abscissa and the ordinate of the individual data,
is the total number of data;
calculating parameter identification coefficients
And
；
drawing by using the parameter identification coefficient
The fitting straight line of (1).
Further, in the dynamic photovoltaic model, the parameters to be identified are specifically the inductance and capacitance of the dynamic model and the series resistance of the parasitic capacitance, and the other parameters are obtained by the static model.
Further, in a linear voltage region of the dynamic photovoltaic model, the series resistance of the photovoltaic unit is smaller than the load resistance.
Further, the reference test was performed with constant solar irradiance and load resistance.
Further, the dynamic photovoltaic model is established for a single photovoltaic cell, and when the dynamic photovoltaic model is extended to the condition of series/parallel connection inside the cell, an equivalent circuit model of one module is obtained.
In summary, the invention provides a dynamic photovoltaic model parameter identification method based on least square regression, wherein the parameter identification process is based on a time domain simulation technology, the method utilizes the step response to the load change in the linear region of the photovoltaic source I-V characteristic, and extracts the parameters of the dynamic part of the model by using the Least Square Regression (LSR) technology of experimental data. The method can effectively isolate the mutual influence of the inductor and the capacitor, and uses a load resistor to prevent the two poles of the circuit from influencing each other. The dynamic parameters can be identified by only measuring the load resistance once. The method for identifying the parameters of the dynamic photovoltaic model based on the least square regression can be suitable for a flexible and variable photovoltaic power generation system, and can realize parameter identification by only one-time measurement, thereby greatly improving the identification efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of a method for identifying parameters of a dynamic photovoltaic model based on least squares regression according to an embodiment of the present invention;
fig. 2 is a complete equivalent circuit diagram of a photovoltaic module according to an embodiment of the present invention;
fig. 3 is an equivalent circuit diagram of a photovoltaic module in a linear voltage region according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the increasing scale and proportion of renewable energy sources connected to a power distribution network, new energy sources on the load side, mainly photovoltaic power generation, have more and more obvious influence on the power grid. The model parameters of the photovoltaic power generation system are the basis for steady-state, quasi-steady-state, and transient calculations, as well as modeling analysis, thereof. The accuracy of the model parameters has a very important influence on the power analysis planning research. Therefore, continuously improving the accuracy of the model parameters is significant to the practical application of the power system.
Research into photovoltaic resources, including issues related to maximizing power production and predicting the behavior of photovoltaic arrays under various environmental conditions, is receiving increasing attention. An accurate photovoltaic source numerical model is helpful to the following aspects: analyzing the power converter of a photovoltaic plant, studying Maximum Power Point Tracking (MPPT) algorithms, etc. In the technical literature, several static photovoltaic models are available for selection. Of these models, the practical power application domain is used to reproduce a model of static I-V and P-V characteristics using a dynamic model of three parameters. These three dynamic parameters (capacitance, inductance and conductance) are not given in the manufacturer's data sheet. Therefore, a simple, fast and accurate parameter identification procedure is needed. However, a method for parameter identification by a single measurement has not been proposed in the technical literature.
In the prior art, a method for identifying parameters is proposed, which includes: establishing a photovoltaic module five-parameter model according to a photovoltaic cell single-diode equivalent schematic diagram, wherein five parameters are respectively a photo-generated current Iph, a diode reverse saturation current I0, an equivalent series resistance Rs, an equivalent parallel resistance Rsh and a diode ideal factor n; deducing the relation among five parameters in a photovoltaic module five-parameter model, and determining an unknown parameter needing to be identified, namely a diode ideal factor n; and thirdly, identifying the ideal factor n of the diode by adopting an improved particle swarm algorithm. The relation between five parameters in the photovoltaic module model is deduced by utilizing information provided by a data manual, so that the unknown parameter to be identified is determined, the parameter identification is carried out by adopting an improved particle swarm algorithm, the algorithm parameter setting is simple, and the method is easy to realize.
However, the method has the disadvantages of low accuracy, low calculation efficiency, long calculation time and large error.
Based on the method, the invention provides a dynamic photovoltaic model parameter identification method based on least square regression.
The following describes an embodiment of the method for identifying parameters of a dynamic photovoltaic model based on least square regression according to the present invention in detail.
Referring to fig. 1, the present embodiment provides a method for identifying parameters of a dynamic photovoltaic model based on least squares regression, which includes the following steps:
s100: and connecting a variable load resistor to the photovoltaic power supply to obtain a dynamic photovoltaic model, wherein the dynamic photovoltaic model utilizes the step response of the photovoltaic power supply I-V characteristic to the load change to carry out parameter identification.
In order to obtain the proper operating conditions for determining the dynamic model parameters, a reference test is required. In the test process, when the pure resistance is subjected to step-type change, a load current waveform is obtained, and a load resistance RL is connected to the photovoltaic power supply so as to draw a proper operation curve.
In an alternative embodiment, the least squares regression-based parameter identification method allows for appropriate dynamic modeling of the photovoltaic source and its connected cable, resulting in several photovoltaic models of static and dynamic parameters, with which better accuracy and precision can be achieved for parameter identification. The model is developed for a single photovoltaic cell, and can be extended to the situation of series/parallel connection in the cell to obtain an equivalent circuit model of a module.
Specifically, as shown in fig. 2, fig. 2 is a complete equivalent circuit diagram of the photovoltaic module. Wherein Iph is a photo-generated current, D is a diode, imod is a filtered current, rs is a series resistance of the photovoltaic unit, rc is a series resistance of the parasitic capacitor, vmod is a filtered voltage, voc is an open-circuit voltage, and I load Is the load current, L is the dynamic model inductance, RL is the load resistance, vload is the load voltage, and C is the capacitance of the dynamic model. The capacitance, conductance and inductance effects of the dynamic model are outlined in fig. 2, and the parameters to be determined are the capacitance C of the dynamic model and the series resistance R of the parasitic capacitance C And the inductance L of the dynamic model. The second order model is also valid in the section of the I-V curve between the open circuit voltage Voc and the maximum power point MPP, which is the usual region in the photovoltaic power generation process.
In the same region, the circuit in fig. 2 has a linear behavior, the current generator and the diode can be replaced by a voltage generator, resulting in the circuit in fig. 3, which is equivalent to a real photovoltaic module in the linear region described above. It is worth noting that any static photovoltaic model is equivalent to the circuit shown in fig. 3 in this area.
In the equivalent circuit of the photovoltaic module of the linear voltage region shown in fig. 3, the equivalent circuit is composed of a four-parameter static model (including the series resistance Rs of the photovoltaic cells, the series resistance Rc of the parasitic capacitance, the open-circuit voltage Voc and the load resistance RL in fig. 3) and a dynamic part (including the dynamic model inductance L and the dynamic model capacitance C in fig. 3), which includes a capacitor having a series resistance and an inductance. Therein, the four parameters of the static model show a dependency on the solar irradiance G and the component temperature T and can be determined using any of the methods proposed in the art.
S200: a variable load resistance value is selected based on the maximum power tracking-the midpoint of the open circuit voltage arc.
In the reference test process, the load current waveform is obtained, and a step change of a pure resistive load is applied to enable the load to change from infinity to a finite value, and the final resistance value corresponds to an operating point belonging to the maximum power tracking-open circuit voltage arc (namely MPP-Voc) region.
S300: and performing reference test on the dynamic photovoltaic model to obtain a load current curve.
In general, by performing a series of tests on real photovoltaic modules having different load resistance values RL, a response corresponding to a pair of complex conjugate poles or two real poles can be obtained. For further parameter acquisition studies to analyze the response for the two real poles. I.e. a reference test is required in order to determine the dynamic model parameters. In this process, a load current waveform can be obtained by applying a step change of the load from infinite to finite value when a step change of a purely resistive load is applied. The resulting resistance value should correspond to an operating point that belongs to the maximum power-open circuit voltage region.
The reference test is performed with a constant solar irradiance G and a load resistance corresponding to an operating point located in the maximum power-open voltage region. For this region there is no interaction between the two poles. We consider that a static model of the measured value of solar irradiance G is known, and then the series resistance Rs of the photovoltaic module is also a known quantity.
To assess how the effect of the uncertainty in the parameter amplitude, obtained by the least two-way regression, affects the value of the dynamic model capacitance C, a sensitivity analysis can be performed. All other quantity parameters are known from the static model, except the series resistance Rc of the dynamic model inductance L, the dynamic model capacitance C and the parasitic capacitance. These values may be considered as constants. In the active area of the proposed model, the series resistance Rs in the constant photovoltaic module is much smaller than the load resistance RL.
S400: after the value of the load resistance is changed until an interactive pole appears, a real curve of the current of the load is drawn, and an error value is selected
And time interval
。
It should be noted that if there is no non-interactive pole, the value of the load resistance RL is increased and the previous step is returned. The identified parameters are to be guaranteed to be valid also in case of actual load. The process of parameter identification is to use the circuit response with the interactive poles. The process of parameter identification cannot be completed without the interactive pole.
S500: based on the selected value of the load resistance, a least squares regression calculation is performed to the left of the load curve minimum.
In the case where the appropriate values of the load resistance RL are determined, the values of all the parameters to be identified are determined by applying the step of least squares regression.
In an alternative embodiment, the parameter identification using least squares regression comprises:
s501: and establishing a calculation model of the inductance time constant, the capacitance time constant, the inductance coefficient and the capacitance coefficient in the dynamic model based on the dynamic photovoltaic model, and calculating the four parameters based on the static parameters and the dynamic parameters by the calculation model.
As can be seen from the figure 3 of the drawings,
wherein, I load Is the current of the load and is,
and
respectively, the inductance and the capacitance coefficient,
and
the inductance and capacitance time constants, respectively, t is the time, and e is the natural constant.
Wherein the content of the first and second substances,
is a voltage of an open circuit, and,
is a load resistance, and is a load resistance,
is a series resistor of the photovoltaic unit.
Because the inductive current can not change in the twinkling of an eye at the commutation, can obtain:
by theoretical analysis of the circuit under consideration, we can find the unknowns in (1), i.e., the time constants of the inductance and capacitance
And
and inductance and capacitance coefficient
And
is calculated as follows:
coefficient of capacitance
：
Inductance factor
：
Time constant of capacitance
：
Time constant of inductance
：
In the above-mentioned formulas, the first and second substrates,
is the series resistance of the parasitic capacitance,
for the inductance in the dynamic model to be,
as is the capacitance in the dynamic model,
is the load current.
The parameter identification process is based on a time domain simulation technique. It makes use of the step response to load changes in the linear region of the I-V characteristic of the photovoltaic source. And the parameters of the dynamic part of the model are extracted using the Least Squares Regression (LSR) technique of the experimental data. The method can effectively isolate the mutual influence of the inductor and the capacitor, and uses a load resistor to prevent the two poles of the circuit from influencing each other. Four parameters, namely time constants of inductance and capacitance, which are required to obtain and characterize a time domain process in time domain simulation
And
and inductance and capacitance coefficient
And
。
s502: and combining every two of the four parameters, and performing fitting calculation of least square regression respectively as horizontal and vertical coordinates of a least square method.
Combining the four parameters in pairs respectively as the horizontal and vertical coordinates of the least square method, and substituting the fitting result of the least square regression into formulas (4) - (7) to obtain the parameter identification value to be identified. The method specifically comprises the following steps:
the first step is as follows: firstly, dimension drawing is carried out on measured values of all photovoltaic operation data, if the shape of the data is approximate to a linear envelope curve, a linear regression method, also called a least square regression method, can be applied.
The second step is that: the average of the abscissa and ordinate of all data points in the graph is calculated using the following formula.
In the formula (I), the compound is shown in the specification,
and
respectively the average of the abscissa and ordinate,
and
are respectively the first
The abscissa and the ordinate of the individual data,
is the total number of data;
the third step: calculating parameter identification coefficients
And
；
drawing by using the parameter identification coefficient
To find errors, if the error is too large, some data is substituted for fitting until the error is expected.
S600: and adjusting the time interval and calculating the actual error until the actual error is smaller than the selected error value to obtain a parameter identification result.
After obtaining the result of parameter identification by using least square regression, if the actual error is too large, the step is skipped back to the fifth step to reduce the time interval
To reduce errors.
The embodiment provides a dynamic photovoltaic model parameter identification method based on least square regression. The parameter identification process is a data processing algorithm based on a suitable time-domain step response and least squares regression, and the equivalent circuit considered is a photovoltaic source model seen at the load terminal. The parameter identification process is based on a time domain simulation technique. It makes use of the step response to load changes in the linear region of the I-V characteristic of the photovoltaic source. And the parameters of the dynamic part of the model are extracted using the Least Squares Regression (LSR) technique of the experimental data. The method can effectively isolate the mutual influence of the inductor and the capacitor, and uses a load resistor to prevent the two poles of the circuit from influencing each other.
Compared with the prior art, the parameter identification method provided by the embodiment has the following advantages:
1. in the embodiment, the equivalent circuit diagram of the photovoltaic module in the linear voltage region as shown in fig. 3 is provided for parameter identification of the dynamic photovoltaic model, so that 5 parameters to be identified can be reduced to 3 parameters, and the calculation efficiency is greatly improved;
2. the dynamic photovoltaic model parameter identification method based on least square regression can enable the identification result to be more accurate;
3. the specific parameter identification process and formula for identifying the dynamic parameters provided in the embodiment can be used for identifying the dynamic parameters, and the photovoltaic power generation system is flexible and variable, so that the embodiment has better accuracy by using the dynamic parameters for identification, and the efficiency is greatly improved by only one-time measurement.
An example of photovoltaic power generation system parameter identification is provided below for illustration.
The example is an analysis of a photovoltaic generator, and the reference values are as follows: the photovoltaic power supply provides 20 per unit values of power, the short-circuit current is 1.34A, the open-circuit voltage is 21V, and the current and voltage at the maximum power point are 1.18A and 16.8V respectively. Can be operated in idle mode or connected to a programmable resistive load. Programmable resistive loads, reference tests are performed at different operating points. A thermometer with the same inclination as the module is installed. The same inclination as the module gives a solar irradiance value of 655 watts per square meter. By applying the parameter identification method of the embodiment to perform calculation, the following results can be obtained:
the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A least square regression-based dynamic photovoltaic model parameter identification method is characterized by comprising the following steps:
connecting a variable load resistor to a photovoltaic power supply to obtain a dynamic photovoltaic model, wherein the dynamic photovoltaic model utilizes a step response of a photovoltaic source I-V characteristic to load change to carry out parameter identification;
selecting a value for one of said variable load resistances based on a maximum power tracking-midpoint of an open circuit voltage arc;
performing reference test on the dynamic photovoltaic model to obtain a load current curve;
after the value of the load resistor is changed until an interactive pole appears, drawing a real curve of the current of the load at the moment and selecting an error value and a time interval;
performing a least squares regression calculation to the left of the load curve minimum based on the selected value of the load resistance;
and adjusting the time interval and calculating the actual error until the actual error is less than the selected error value to obtain a parameter identification result.
2. The method for identifying parameters of a dynamic photovoltaic model based on least squares regression as claimed in claim 1, wherein the dynamic photovoltaic model is obtained by simplifying a complete equivalent circuit of a photovoltaic module, and a voltage generator is used to replace a current generator and a diode in the complete equivalent circuit of the photovoltaic module to obtain the equivalent circuit of the photovoltaic module in a linear voltage region, namely the dynamic photovoltaic model.
3. The method for identifying parameters of a dynamic photovoltaic model based on least squares regression as claimed in claim 2, wherein the dynamic photovoltaic model specifically comprises: a static model and a dynamic model;
the static model comprises four static parameters, specifically a series resistance of a photovoltaic unit, a series resistance of a parasitic capacitor, an open-circuit voltage and the load resistance;
the dynamic model includes two dynamic parameters, specifically, inductance and capacitance.
4. The method for identifying parameters of a dynamic photovoltaic model based on least squares regression of claim 3, wherein based on the selected value of the load resistance, a least squares regression calculation is performed on the left side of the minimum value of the load curve, specifically comprising:
establishing a calculation model of an inductance time constant, a capacitance time constant, an inductance coefficient and a capacitance coefficient in the dynamic model based on the dynamic photovoltaic model, wherein the calculation model calculates the four parameters based on the static parameters and the dynamic parameters;
and combining every two of the four parameters, and respectively using the four parameters as horizontal and vertical coordinates of a least square method to perform fitting calculation of least square regression.
5. The method for identifying parameters of a dynamic photovoltaic model based on least squares regression as claimed in claim 4, wherein the calculation model established based on the dynamic photovoltaic model is specifically as follows:
coefficient of capacitance
：
Coefficient of inductance
：
Time constant of capacitance
：
Time constant of inductance
：
In the above-mentioned formulas, the first and second substrates,
in order to be the open-circuit voltage,
as the load resistance, there is a resistance of the load,
is the series resistance of the parasitic capacitance,
is the inductance in the dynamic model and is,
is the capacitance in the dynamic model and is,
in order to be the load current,
wherein, in the step (A),
is a stand forThe photovoltaic unit is connected with the resistor in series.
6. The method for identifying parameters of a dynamic photovoltaic model based on least squares regression as claimed in claim 4, wherein two of the four parameters are combined and respectively used as the abscissa and ordinate of the least squares method to perform the fitting calculation of the least squares regression, specifically comprising:
drawing the dimensionality of the measured values of all photovoltaic operation data;
calculating the average value of the horizontal and vertical coordinates of all data points in the graph according to the following formula;
in the formula (I), the compound is shown in the specification,
and
respectively the average of the abscissa and ordinate,
and
are respectively the first
The abscissa and the ordinate of the individual data,
is the total number of data;
calculating parameter identification coefficients
And
；
drawing by using the parameter identification coefficient
The fitting straight line of (1).
7. The method as claimed in claim 3, wherein in the dynamic photovoltaic model, the parameters to be identified are inductance and capacitance of the dynamic model and series resistance of the parasitic capacitance, and the rest of the parameters are obtained from the static model.
8. The method of claim 3, wherein the photovoltaic cell series resistance is less than the load resistance in a linear voltage region of the dynamic photovoltaic model.
9. The method of least squares regression based dynamic photovoltaic model parameter identification as claimed in claim 1 wherein the reference test is performed with constant solar irradiance and load resistance.
10. The method for identifying parameters of a dynamic photovoltaic model based on least squares regression as claimed in claim 1, wherein the dynamic photovoltaic model is built for a single photovoltaic cell, and when the dynamic photovoltaic model is extended to the case of series/parallel connection inside the cell, an equivalent circuit model of one module is obtained.
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